A plurality of people faces problems due to sleep abnormalities and sleep disturbances. For example sleep abnormalities or pathologies are numerous and may be as different as narcolepsy, sleepwalking, or abnormal sleep lengths such as insomnia or hypersomnia. Moreover, the sleep of a person may be disturbed by snoring, which is often associated with the obstructive sleep apnoea syndrome, or by environmental factors, such as light or noise. These sleep disturbances are generally marked by the occurrence of sleep events which combine sudden changes of physiological variables such as autonomic (respiratory or cardiac) or motor modifications. The sleep events can also be caused by symptoms of sleep pathologies such as sleep apnoea, restless leg, abnormal movement, sleepwalking, erratic heart rate, nightmare, night terror, etc. Thus, a snoring sleeper or talking and screaming during nightmare or night terror will usually cause an abnormal sleep event. The consequences of abnormal or disturbed sleep are numerous from a health (care) but also from a social-economic point of view.
In order to detect the reasons of the peoples' sleep abnormalities and disturbances, sleep laboratories can conduct a sleep scoring of the person, i.e. the determination of sleep stages and their transitions. In a sleep laboratory, physiological parameters are observed and corresponding data recorded in a polysomnography. This recording during a polysomnography includes primary data such as electroencephalograms (EEG), electrooculogram (EOG) and electromyogram (EMG), and secondary data such as heart rate, respiration, oximetry and body movements. EEG is used to detect and name brainwaves according to their frequency and amplitude. With an EOG the movement of the eye balls is recognized and analysed. EMG allows for evaluating and recording the electrical activity produced by skeletal muscles.
Classically, sleep scoring is based on the analysis of EEG, EOG and EMG recordings made continuously throughout the sleep period. These physiological data are represented by fluctuations of electrical potentials recorded by small electrodes attached to different parts of the scalp and the face of the tested/recorded person.
Those electrical potentials are then interpreted by a sleep specialist according to internationally accepted rules which define the different stages of sleep. Each sleep stage is characterized by the presence and the abundance of specific EEG waves on the recording. Further, eye movements detected by the EOG recording are mainly present during the Rapid Eye Movement (REM) sleep stage, while EMG shows variations in both its tonic and phasic levels depending on the sleep stage and the simultaneous presence of body movements.
A polysomnography provides various disadvantages. For instance, during an EEG electric potentials are recorded by using electrodes fixed on several sides of the skull, e.g. the electrodes are glued to the face and on the skull.
Also, EOGs and EMGs require the attaching of electrodes and sensors to the face, skull or other body parts of the tested person. To detect eye movement during sleep, an EOG requires electrodes glued or otherwise attached near the eye or on the eyelid of the person.
All these electrodes further require wires that are attached to the electrodes and lead to a device placed near the head of the bed limiting the movement freedom of the tested person. Such recording is therefore obtrusive due to wiring, unusual sleep environments and imposed schedule at bedding conditions. The results of such tests may therefore be distorted due to the changed environment of the tested person.
In addition, polysomnographies provide limitations due to the complexity of the recording techniques. In detail, specific recording places such as sleep laboratories and special equipment as well as well trained staff are necessary. Therefore, polysomnography remains an exceptional and expensive method for sleep evaluation.
A polysomnography system based on detection of eyelid movement (EOG), head movement and a heart beat signal (electrocardiogram—ECG) is described in U.S. Pat. No. 5,902,250. The described system, however, is expensive and creates sleep disturbances due to the amount of sensors and wires necessary. In addition, the system described in U.S. Pat. No. 5,902,250 is not very precise in determining sleep stages and does not determine sleep stage transitions.
Further, U.S. Pat. No. 7,351,206 relates to a sleep state determining apparatus that determines a sleep state based on a series of pulse interval data. Body movement data is determined to remove pulse interval data from the series of pulse interval data that was measured in parallel with the body movement data, if a fluctuation amount of the body movement data is greater than a predetermined threshold. The lacking data lead to imprecise sleep stage determination if the body movements are numerous or of long duration. Thus, the derived results may not be sufficient to reliably score sleep stages.
WO 98/43536 A1 discloses a method for determining the sleep state of a patient. The method includes monitoring the heart rate variability of the patient, and determining the sleep state based on the heart rate variability. The method also may include monitoring the frequency of eyelid movements, and making the sleep state determination based also on the frequency of eyelid movements. A method for determining respiratory pattern includes monitoring heart rate variability by receiving heart beat signals, and determining respiratory pattern from the strength of the signals. A home-based, wearable, self-contained system determines sleep-state, respiratory pattern, assesses cardiorespiratory risk, of a patient based on the frequency of eyelid movements, the frequency of head movements, and heart rate variability.
US 2007/0106183 A1 discloses a sleep state measuring apparatus with an autonomic nerve index obtaining unit that obtains a user's autonomic nerve index; and a sleep periodicity index calculating unit that calculates a sleep periodicity index based on a temporal change of the autonomic nerve index and a change in a user's sleeping cycle, wherein the sleep periodicity index indicates whether the user is sleeping or not according to a user's ideal sleeping cycle as an index, or a dominance index calculating unit that calculates a parasympathetic nerve dominance index which shows dominance of a parasympathetic nerve index included in the autonomic nerve index with respect to a sympathetic nerve index included in the autonomic nerve index for a user during sleep.
US 2009/0264715 A1 discloses a sleep system having sensors capable of gathering sleep data from a person and environmental data during a sleep by the person. A processor executes instructions that analyze this data and control the sleep of the person and the environment surrounding the person. Typically, the instructions are loaded in a memory where they execute to generate an objective measure of sleep quality from the sleep data from the person and gather environmental data during the sleep by the person. Upon execution, the instructions receive a subjective measure of sleep quality from the person after the sleep, create a sleep quality index from the objective measure of sleep quality and subjective measure of sleep quality, correlate the sleep quality index and a current sleep system settings with a historical sleep quality index and corresponding historical sleep system settings. The instructions then may modify the current set of sleep system settings depending on the correlation between the sleep quality index and the historic sleep quality index. These sleep system settings control and potentially change one or more different elements of an environment associated with the sleep system.
US 2010/0125215 A1 discloses a sleep analysis system and a method for analysis thereof. The sleep analysis system includes an analysis device and a sleep sensing apparatus. The sleep sensing apparatus includes an ECG signal collector, a multi-axial accelerometer, a wireless transmitting unit, and a control unit. The ECG signal collector is used for collecting an ECG signal associated with a subject. The multi-axial accelerometer is used for detecting a multi-axial accelerometer signal associated with the subject. The control unit controls the wireless transmitting unit to transmit the ECG signal and the multi-axial accelerometer signal to the analysis device for analyzing sleep of the subject. No distinction is made between stages of Non-REM sleep.
However, due to large uncertainties in determining sleep stages and/or sleep stage transitions compared to classical visual sleep scoring, the percent agreement between these sleep scoring approaches with the classical visual sleep scoring approach is considered as too low by the sleep researchers and sleep clinicians. Therefore, these techniques are not in use yet in the medical world.
It is an object of the invention to provide a system and method for determining sleep states and/or sleep stages and/or sleep stage transitions which reduces the disturbance of the sleep of the tested person and provides reliable results while being sufficiently precise and inexpensive.
This object is solved by the present invention as defined by the independent claims. Preferred embodiments are defined by the dependent claims.
Heart rate variability and a LF/HF ratio of a low-frequency (LF) to a high-frequency (HF) component of heart rate variability (HRV) signal may be used to determine sleep state and the sleep stages. Non transient (stationary) fluctuations in heart rate allow a differentiation of the sympathetic and parasympathetic activation, which are related to a low-frequency (LF) and a high-frequency (HF) component of a heart rate variability (HRV) signal. The resulting LF/HF ratio is a quantitative index of the sympatho-vagal balance and can be computed by means of spectral analysis. The more synchronized the sleep is, the more the LF/HF ratio decreases, whereas the LF/HF ratio is significantly increased during REM sleep, indicating a sympathetic predominance during this period. Thus, spectral analysis of the HRV provides additional information of the ultradian rhythmic behaviour of the autonomic nervous system function beyond the traditional cardiovascular measurements (mean heart rate, blood pressure, etc.).
A disadvantage of this spectral analysis approach is that this ratio must be calculated when the heart rate signal is stationary. When the person is moving, the calculation of this ratio is polluted by the changes in heart rate induced by the movements. In other words the LF/HF ratio can only be used when the person remains still.
Scoring sleep is based not only on the determination of a particular sleep stage but also on determining transitions from one stage to another stage. Determining a sleep state, a sleep stage and/or a sleep stage transition using the LF/HF ratio, in particular determining the exact time of the sleep stage and/or the transition from a sleep stage to another sleep stage is highly problematic and the uncertainty in the determination of the sleep stage and/or the stage transition might be of a few to several minutes when the heart rate is not stationary enough.
For example, there may be slow transitions of the LF/HF ratio, with the LF/HF ratio fluctuating like a sine wave from high to low values and reverse. However, if there is no information on the value of the LF/HF ratio indicating a transition between the sleep stages, transitions from Non-REM to REM sleep stages might be arbitrarily fixed by a horizontal line cutting this fluctuating curve. However, using such a technique, the time of the transition can not be determined precisely enough compared to classical visual sleep scoring and therefore the accuracy of sleep stage determination is not sufficient.
An embodiment of the present invention uses heart rate and body movements to determine a sleep state, a sleep stage and/or a sleep stage transition. In a preferred embodiment, determining a sleep stage and/or sleep stage transition is based on heart rate and body movement classes. For example, a sleep stage transition can be precisely determined by looking simultaneously at the level and the suddenly occurring changes in heart rate and the possible concomitant body movements. If no sign of transition is observed, the person remains in the same state (awake or asleep) or in the same sleep stage in the latter case. In doing so, embodiments of the present invention are not dependent on any stationarity of the heart rate signals and by using their sudden modifications, a transition from one stage to another stage can be determined with an uncertainty of a few seconds.
In accordance with a preferred embodiment, the present invention relates to a system for determining sleep, a sleep stage and/or a sleep stage transition of a person. The system includes heart rate detecting means configured for detecting a heart rate of the person and movement detecting means configured for detecting a movement of a part of the body of the person. The detected movement is caused by a skeletal muscle of the body. The system further includes recording means configured for recording the detected heart rate and the detected movement of the part of the body, heart rate classifying means configured for classifying the recorded heart rate of the person into at least one heart rate class and movement classifying means configured for classifying the recorded movement into at least one movement class. The system also includes determining means configured for determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class.
According to an aspect of this embodiment, the system includes heart rate calculating means configured for calculating a heart rate average, a variability value (including the classically used spectral LF/HF ratio), a rhythm characteristic and/or a heart rate event or change from the recorded heart rate. The heart rate classifying means are configured for classifying the heart rate of the person based on the calculated heart rate average, variability value, rhythm characteristic and/or heart rate event or change.
In a further aspect of the embodiment, the determining means are configured for identifying a specific combination of a heart rate class and a movement class within a specific time period, and the determining means are configured for determining sleep, a sleep stage, sleep stage transition and/or a sleep event based on the identified specific combination.
With respect to an aspect of this embodiment, the movement detecting means comprise movement sensing means configured for sensing an acceleration of a part of the body of the person, where the recording means are further configured for recording the sensed acceleration. The system includes movement calculating means configured for calculating, based on values of the recorded acceleration, at least an intensity and/or a duration of each movement of the part of the body of the person.
According to a further aspect of the embodiment, the movement classifying means are configured for classifying each movement of the part of the body at least into a large movement (LM), a small movement (SM) or a twitch (TM), based on the calculated intensity and/or duration of each movement, and/or configured for classifying each LM, SM and/or TM at least into frequency classes and/or duration classes.
In accordance with another aspect of this embodiment, the system further includes environmental sensing means configured for sensing at least one environmental factor, where the recording means are further configured for recording the sensed at least one environmental factor, and environmental classifying means configured for classifying at least some values of the at least one recorded environmental factor into at least one environmental class. The determining means are further configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one environmental class.
According to an aspect of this embodiment, the environmental sensing means are configured for sensing a noise level, an ambient temperature and/or an ambient light.
In accordance with yet another aspect of the embodiment, the system further includes environmental calculating means configured for calculating at least one average noise level and/or noise event based on the recorded noise level, and/or calculating at least one average ambient temperature level and/or change and/or variation based on the recorded ambient temperature, and/or calculating at least one ambient light level and/or change and/or variation of ambient light level based on the recorded ambient light.
With respect to another aspect of this embodiment, the determining means are further configured for determining a transition from waking to sleeping and/or a transition from one sleep stage to another and/or a transition from sleeping to waking and/or a direct causal effect of at least one recorded environmental factor on a sleep stage transition or a transition from sleeping to waking.
In accordance with an aspect of the embodiment, the system further includes evaluating means configured for evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class, the at least one environmental class and/or any combination thereof.
According to a further embodiment, a system for determining sleep, a sleep stage and/or a sleep stage transition of a person comprises a heart rate detecting means configured for detecting a heart rate of the person, and a movement detecting means configured for detecting a movement of a part of the body of the person, wherein the movement is caused by a skeletal muscle of the body. The system further comprises a recording means configured for recording the detected heart rate and the detected movement of the part of the body, a heart rate classifying means configured for classifying the recorded heart rate of the person into at least one heart rate class and at least one heart rate variability class, and a movement classifying means configured for classifying the recorded movement into at least one movement class. The system further comprises determining means configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class, the at least one heart rate variability class, and the at least one movement class, wherein the determining means are configured for identifying a combination of a heart rate class, a heart rate variability class, and a movement class within a time interval, and for determining sleep, a sleep stage and/or a sleep stage transition based on the identified combination.
According to a preferred embodiment, the at least one heart rate class comprises a heart rate average class.
In a preferred embodiment, heart rate average classification is based on a heart rate average, with the heart rate average being averaged over a predetermined time interval. Preferably, the predetermined time interval for averaging the heart rate varies depending on a detected body movement on the person. For example, in a preferred embodiment, the heart rate average is calculated by averaging the heart rate over a first time interval if there is some body movement, and is calculated by averaging the heart rate over a second time interval if there are no or few body movements, with the second time interval being longer than the first time interval.
According to a further embodiment, a system for determining sleep, a sleep stage and/or a sleep stage transition of a person includes a wearable device configured for detecting and recording a heart rate of the person and configured for detecting and recording a movement of a part of the body of the person, where the movement is caused by a skeletal muscle of the body, and an analysis device configured for classifying the recorded heart rate of the person into at least one heart rate class, configured for classifying the recorded movement into at least one movement class, and configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class. The system also includes a data connection configured for communicating data representing the recorded heart rate and recorded movement from the wearable recording device to the analysis device.
In accordance with yet another embodiment, a method for determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of a person comprises the steps of detecting a heart rate of the person, recording the detected heart rate, detecting a movement of a part of the body of the person, where the movement is caused by a skeletal muscle of the body, recording the detected movement, classifying the recorded heart rate of the person into at least one heart rate class, classifying the recorded movement into at least one movement class, determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class.
According to an aspect of this embodiment, the method comprises identifying a specific combination of a heart rate class and a movement class within a specific time period, where determining comprises determining the sleep stage based on the identified specific combination.
With respect to another aspect of the embodiment, the method comprises sensing at least one environmental factor, recording the sensed at least one environmental factor, classifying at least some values of the at least one recorded environmental factor into at least one environmental class, and determining a sleep event of the person based at least partially on the at least one environmental class.
According to an aspect of the embodiment, the method includes evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class, the at least one environmental class and/or any combination thereof.
In accordance with yet another aspect of the embodiment, the method comprises determining a direct causal effect of at least one recorded environmental factor on a sleep stage transition or a sleep event or a transition from sleeping to waking based at least partially on the at least one environmental class.
Regarding a further embodiment, a system for determining sleep, a sleep stage and/or sleep stage transition of a person includes heart rate detecting means configured for detecting a heart rate of the person, movement detecting means configured for detecting a movement of a part of the body of the person, where the movement is caused by a skeletal muscle of the body, heart rate classifying means configured for classifying the detected heart rate of the person into at least one heart rate class, movement classifying means configured for classifying the detected movement into at least one movement class, and determining means configured for determining sleep, sleep stage and/or a sleep stage transition of the person based at least partially on the at least one heart rate class and the at least one movement class.
With respect to an aspect of this embodiment, the system includes heart rate calculating means configured for calculating a heart rate average, a variability value (including the spectral LF/HF ratio), a rhythm characteristic and/or a heart rate event or change from the detected heart rate, where the heart rate classifying means are configured for classifying the heart rate of the person based on the calculated heart rate average, variability value, rhythm characteristic and/or heart rate event or change.
In accordance with another aspect of the embodiment, the determining means are further configured for identifying a specific combination of a heart rate class and a movement class within a specific time period, where the determining means are configured for determining sleep, the sleep stage and/or the sleep stage transition and/or a sleep event based on the identified specific combination.
In accordance with yet another aspect of the embodiment, the determining means are configured for identifying a successive order of the heart rate class and the movement class of the specific combination.
Regarding another aspect of the embodiment, the determining means are configured for identifying, as a specific combination, a heart rate acceleration event together with a movement of the part of the body, a heart rate acceleration event preceding a movement of the part of the body, a heart rate acceleration event without a movement of the part of the body within the specific time period, and/or a heart rate acceleration event after a movement of the part of the body.
According to another aspect of the embodiment, the heart rate detecting means comprise pulse wave sensing means configured for sensing a pulse wave of the heart of the person.
In accordance with a further aspect of the embodiment, the movement detecting means comprise movement sensing means configured for sensing an acceleration of the part of the body of the person, where the system includes movement calculating means configured for calculating, based on values of the sensed acceleration, at least an intensity and/or a duration of each movement of the part of the body of the person.
In accordance with another aspect of the embodiment, the movement classifying means are configured for classifying each movement of the part of the body into at least a large movement, a small movement or a twitch, based on the calculated intensity and/or duration of each movement.
In accordance with yet another aspect of the embodiment, the movement classifying means are configured for classifying each movement of the part of the body at least into a large movement (LM), a small movement (SM) or a twitch (TM), based on the calculated intensity and/or duration of each movement, and/or configured for classifying each LM, SM and/or TM at least into frequency classes and/or duration classes.
According to another aspect of the embodiment, the system further includes environmental sensing means configured for sensing at least one environmental factor, and environmental classifying means configured for classifying at least some values of the at least one sensed environmental factor into at least one environmental class.
According to yet another aspect of the embodiment, the determining means are further configured for determining a sleep event of the person based at least partially on the at least one environmental class.
Regarding a further aspect of the embodiment, the environmental sensing means are configured for sensing a noise level, an ambient temperature and/or an ambient light.
With respect to yet a further aspect of the embodiment, the system further includes environmental calculating means configured for calculating at least one average noise level and/or noise event based on the sensed noise level, and/or calculating at least one average ambient temperature level and/or a change and/or variation of the sensed ambient temperature, and/or calculating at least one ambient light level and/or change of ambient light level based on the sensed ambient light.
According to another aspect of the embodiment, the determining means are further configured for determining a transition from one sleep stage to another and/or from a sleep stage to wake and/or a sleep event.
According to yet another aspect of the embodiment, the determining means are configured for determining that the transition is a descending transition or an ascending transition, where a descending transition starts from waking or from a lighter sleep stage and leads to a deeper sleep stage, and wherein an ascending transition starts from a deeper sleep stage and leads to a lighter sleep stage or to waking.
In accordance with an aspect of the embodiment, the system further includes identifying means configured for identifying a missing value and/or an abnormal value within the detected heart rate, the detected movement and/or the values of the at least one sensed environmental factor.
Regarding another aspect of the embodiment, the system further includes evaluating means configured for evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class, the at least one environmental class and/or any combination thereof.
According to yet another embodiment, a system for determining sleep, a sleep stage and/or a sleep stage transition of a person includes a wearable device configured for detecting a heart rate of the person and configured for detecting a movement of a part of the body of the person, where the movement is caused by a skeletal muscle of the body, an analysis device configured for classifying the detected heart rate of the person into at least one heart rate class, configured for classifying the detected movement into at least one movement class, and configured for determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class, and a data connection configured for communicating data representing the detected heart rate and detected movement from the wearable recording device to the analysis device.
In accordance with an aspect of this embodiment, the identifying means are further configured for recuperating missing data and/or abnormal data.
In accordance with another aspect of the embodiment, the data connection is a wireless data connection.
According to yet another aspect of the embodiment, the wearable device is worn by the person at a limb, the torso and/or the head of the person.
With respect to another aspect of the embodiment, the wearable device is further configured for recording data representing at least successive heart rate intervals, and is configured for recording data representing the detected movement, where the data connection is configured for communicating the recorded data representing at least successive heart rate intervals and/or data representing the detected movement from the wearable device to the analysis device.
Regarding another aspect of the embodiment, the analysis device is further configured for evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class and/or any combination thereof.
According to a further embodiment, a method for determining sleep, the sleep stage and/or a sleep stage transition of a person comprises the steps of detecting a heart rate of the person, detecting a movement of a part of the body of the person, where the movement is caused by a skeletal muscle of the body, classifying the detected heart rate of the person into at least one heart rate class, classifying the detected movement into at least one movement class, and determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class.
In accordance with an aspect of this embodiment, the method comprises the step of identifying a specific combination of a heart rate class and a movement class within a specific time period, where determining comprises determining sleep, the sleep stage and/or a sleep stage transition and/or a sleep event based on the identified specific combination.
In accordance with another aspect of the embodiment, the method comprises calculating a heart rate average, a variability value, a rhythm characteristic and/or a heart rate event or change from the detected heart rate, where classifying the heart rate of the person comprises classifying the heart rate based on the calculated heart rate average, variability value, rhythm characteristic and/or heart rate event or change.
According to another aspect of the embodiment, the method comprises the steps of sensing at least one environmental factor and classifying at least some values of the at least one sensed environmental factor into at least one environmental class.
Regarding a further aspect of the embodiment, determining comprises determining a sleep event of the person based at least partially on the at least one environmental class.
According to yet another aspect of the embodiment, sensing the at least one environmental factor comprises sensing a noise level, an ambient temperature and/or an ambient light.
According to an aspect of the embodiment, the method includes evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class, the at least one environmental class and/or any combination thereof.
In accordance with a further embodiment, the present invention relates to a system for determining sleep, a sleep stage and/or a sleep stage transition of a person. The system includes a heart rate detector configured for detecting a heart rate of the person and movement detector configured for detecting a movement of a part of the body of the person, where the detected movement is caused by a skeletal muscle of the body. The system further includes a recording unit configured for recording the detected heart rate and the detected movement of the part of the body, a heart rate classifying unit configured for classifying the recorded heart rate of the person into at least one heart rate class and a movement classifying unit configured for classifying the recorded movement into at least one movement class. The system also includes a determining unit configured for determining sleep, a sleep stage and/or a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class.
According to an aspect of this embodiment, the system includes a heart rate calculating unit configured for calculating a heart rate average, a variability value, a rhythm characteristic and/or a heart rate event or change from the recorded heart rate. The heart rate classifying unit is configured for classifying the heart rate of the person based on the calculated heart rate average, variability value, rhythm characteristic and/or heart rate event or change.
In a further aspect of the embodiment, the determining unit is configured for identifying a specific combination of a heart rate class and a movement class within a specific time period, and the determining unit is configured for determining sleep, the sleep stage and/or sleep stage transition and/or a sleep event based on the identified specific combination.
With respect to an aspect of this embodiment, the movement detecting unit comprises a movement sensor configured for sensing an acceleration of the part of the body of the person, where the recording unit is further configured for recording the sensed acceleration. The system includes a movement calculating means configured for calculating, based on values of the recorded acceleration, at least an intensity and/or a duration of each movement of the part of the body of the person.
According to a further aspect of the embodiment, the movement classifying means is configured for classifying each movement of the part of the body at least into a large movement (LM), a small movement (SM) or a twitch (TM), based on the calculated intensity and/or duration of each movement, and/or configured for classifying each LM, SM and/or TM at least into frequency classes and/or duration classes.
In accordance with another aspect of this embodiment, the system further includes an environmental sensor configured for sensing at least one environmental factor, where the recording unit is further configured for recording the sensed at least one environmental factor, and an environmental classifying unit configured for classifying at least some values of the at least one recorded environmental factor into at least one environmental class. The determining unit is further configured for determining a sleep event of the person based at least partially on the at least one environmental class.
According to an aspect of this embodiment, the environmental sensor is configured for sensing a noise level, an ambient temperature and/or an ambient light.
In accordance with yet another aspect of the embodiment, the system further includes an environmental calculating unit configured for calculating at least one average noise level and/or noise event based on the recorded noise level, and/or calculating at least one average ambient temperature level and/or change and/or variation based on the recorded ambient temperature, and/or calculating at least one ambient light level and/or change of ambient light level based on the recorded ambient light.
With respect to another aspect of this embodiment, the determining unit is further configured for determining an ascending transition from one sleep stage to another and/or a transition from sleeping to waking and/or a direct causal effect of at least one recorded environmental factor or a sleep event on an ascending sleep stage transition and/or a transition from sleeping to waking.
In accordance with an aspect of the embodiment, the system further includes an evaluating unit configured for evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one movement class, the at least one environmental class or any combination thereof.
According to a further embodiment, the present invention provides a method for determining sleep, a sleep stage and/or a sleep stage transition of a person. The method comprises detecting a heart rate of the person, recording the detected heart rate, detecting a movement of a part of the body of the person, wherein the movement is caused by a skeletal muscle of the body, recording the detected movement, classifying the recorded heart rate of the person into at least one heart rate class and at least one heart rate variability class, classifying the recorded movement into at least one movement class; and determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class, the at least one heart rate variability class, and the at least one movement class.
According to a preferred embodiment of the method, the at least one heart rate class comprises a heart rate average class and a heart rate average classification.
In a preferred embodiment, heart rate average classification is based on a heart rate average, with the heart rate being averaged over a predetermined time interval. Preferably, the time interval for calculating the heart rate average varies depending on a detected body movement on the person. For example, in a preferred embodiment, the heart rate average is calculated by averaging the heart rate over a first time interval if there is some body movement, and is calculated by averaging the heart rate over a second time interval if there are no or few body movements, with the second time interval being longer than the first time interval.
Aspects of different embodiments of the present invention can be combined unless stated otherwise.